Electronic Journal of Plant Breeding
Open Access
SCOPUS
  • Year: 2023
  • Volume: 14
  • Issue: 1

Study of genetic variability and diversity analysis in maize (Zea mays L.) by agglomerative hierarchical clustering and principal component analysis

  • Author:
  • H. Fathima Sinana1, R. Ravikesavan1,*, K. Iyanar2, A. Senthil3
  • Total Page Count: 9
  • Page Number: 43 to 51

1Department of Genetics and Plant Breeding, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

2Department of Millets, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

3Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

*E-Mail: chithuragul@gmail.com

Online published on 17 April, 2023.

Abstract

The most comprehensive study was undertaken to investigate genetic variability, character association studies and diversity analysis for yield and yield attributing traits in sixty-fve maize genotypes along with fve checks raised in Augmented block design II and observations were recorded on fourteen morphological traits. For all the traits, the analysis of variance revealed signifcant variance. PCV and GCV was high for anthesis silking interval. High genetic advance as per cent of mean along with heritability was recorded for anthesis silking interval, number of tassel branches, cob length, plant height and tassel height. This showed the efectiveness of selection for these traits. Character association analysis confrmed that grain yield per plant had a positive signifcant association with number of kernel rows per cob, number of kernels per row, cob breadth, cob length, 100 seed weight, shelling percentage and plant height. This implies that by selecting for these traits, grain yield per plant can be improved signifcantly. Path analysis for the attributed traits showed the direct efect of shelling percentage and plant height on single plant yield. Consequently, an emphasis on these traits could lead to the successful identifcation of higher yielding genotypes. Based on agglomerative hierarchical clustering, genotypes UMI1113, UMI1153, UMI 653-2-3, UMI1131-5 and UMI1076-5-4-2 can be used as parental lines in the hybridization programme. PCA biplot showed that number of kernel rows per cob, number of kernels per row, cob placement height, plant height and single plant yield mostly contributed towards variability. Furthermore, these parental lines should be assessed for their general combining ability and specifc combining ability to generate stable and superior hybrids for better yield performance.

Keywords

Genetic variability, Character association, Diversity analysis